Cardiovascular diseases (CVDs) have been the No. 1 killer in the United States for almost every year of the past century. CVDs are also prevalent all over the world, especially among the elderly. Therefore, a need exists to develop effective cardiovascular monitoring techniques that provide information for early diagnosis and treatment of CVDs. The ultimate goal of cardiovascular monitoring is to evaluate cardiac performance, i.e., how well the heart is functioning. This is traditionally done by an invasive, expensive, and sometimes dangerous catheterization procedure. Because of the disadvantages, doctors use catheterization only for critically ill patients.
When catheterization is not utilized, cardiac performance is evaluated from information obtained by noninvasive monitoring, as described in Fetics, B. et al., “Parametric Model Derivation of Transfer Function for Noninvasive Estimation of Aortic Pressure by Radial Tonometry”, IEEE Trans. on Biomed. Eng., Vol. 46, No. 6, pp. 698–706 (1999). In hospitals, doctors derive the cardiac information from noninvasive peripheral measurements based various auxiliary measurements. For example, four of the most frequently used vital signs in cardiovascular monitoring are heart rate, electrocardiogram signals, blood pressure and oxygen saturation. Doctors combine this information together with other observations to judge how well the patient's heart is working. There are two disadvantages with this method. First, the method is based solely on localized data, wherein systemic information is not utilized. Second, the method involves discrete monitoring since doctors and nurses can only check a patient's readings every few hours; thus dynamic information is not considered.
Utilizing current model-based techniques, the central pressure can be estimated by mathematically transforming peripheral pressure measurements that can be measured non-invasively. This is inverse monitoring. The objective is to extract information about the central cardiovascular system characteristics from peripheral noninvasive measurements. Inverse monitoring requires deriving a general model from the central system characteristics to the peripheral pressure. First, invasive central measurements and noninvasive peripheral measurements are taken for a group of subjects. Second, the transfer function for each subject in the group is identified. Finally, the average of all the transfer functions is used as a general model for predictions on other subjects. Inverse monitoring is better than traditional clinical monitoring in that it is model-based, and therefore more systematic. As well, it provides continuous monitoring, and therefore all the trends and dynamics can be captured. Inverse monitoring, however, has two major disadvantages. First, an invasive central measurement is still necessary to identify the individual transfer functions from the central pressure, system input, to the peripheral pressure, system output. Second, an averaged transfer function is used to predict the input from the output on a particular subject. Since the characteristics of a cardiovascular system are highly time-variable and subject-dependent, such a general model will not work well for everyone.